Isoform TPMs - KD6 Essential
Mervin M Fansler
10 December 2022
Purpose
Given the clusters of regulating targets and regulated response genes, we want to examine to what extent these are changes in expression levels of particular isoforms.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(pheatmap)
library(SummarizedExperiment)Parameters
set.seed(20210818)
## input files
FILE_WUI="tbl/df_wui_kd6_essential_ui10.Rds"
CSV_TARGETS="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
CSV_GENES="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
NULL_CLUSTERS=as.character(c(9,12,13))
idx_clusters_targets <- as.character(c(14,18,11,16,13,9,12,2,5,15,1,6,8,3,7,4,10,17))
idx_clusters_genes <- as.character(c(20,16,7,8,1,3,18,2,5,10,15,19,14,11,4,6,17))
## output files
FILE_OUT_SU_FINAL="img/heatmap-su-kd6-clusters.pdf"
FILE_OUT_LU_FINAL="img/heatmap-lu-kd6-clusters.pdf"
## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_l2fc <- seq(-2, 2, length.out=NCOLORS + 1)Data
Loading Data
df_targets <- read_csv(CSV_TARGETS, col_types='cccc')
df_genes <- read_csv(CSV_GENES, col_types='ccc')
df_wui <- readRDS(FILE_WUI)Preprocessing
sgid2gene <- df_wui %>%
dplyr::select(sgID_AB, target_gene) %>%
distinct(sgID_AB, target_gene) %>%
deframe()
ens2gene <- df_wui %>%
dplyr::select(gene_id, gene_name) %>%
distinct(gene_id, gene_name) %>%
deframe()
convert_rownames <- function (mat, in2out) {
mat %>% set_rownames(in2out[rownames(.)])
}
convert_colnames <- function (mat, in2out) {
mat %>% set_colnames(in2out[colnames(.)])
}
df_ntp <- filter(df_wui,
target_gene == "non-targeting",
gene_id %in% df_genes$gene_id) %>%
group_by(gene_id, gene_name) %>%
filter(!is.na(wui)) %>%
summarize(mean_tpm_su=weighted.mean(tpm_su, n_cells),
sd_tpm_su=sqrt(sum((tpm_su-mean_tpm_su)^2)/n()),
mean_tpm_lu=weighted.mean(tpm_lu, n_cells),
sd_tpm_lu=sqrt(sum((tpm_lu-mean_tpm_lu)^2)/n()),
mean_wui=weighted.mean(wui, n_cells),
.groups='drop')
df_l2fc <- df_wui %>%
filter(sgID_AB %in% df_targets$sgID_AB,
gene_id %in% df_genes$gene_id) %>%
inner_join(df_ntp, by=c("gene_id", "gene_name")) %>%
mutate(l2fc_su=log2(tpm_su/mean_tpm_su),
l2fc_lu=log2(tpm_lu/mean_tpm_lu)) %>%
dplyr::select(gene_id, sgID_AB, tpm_su, l2fc_su, tpm_lu, l2fc_lu, mean_wui)
l2fc_su_target_gene <- df_l2fc %>%
dplyr::select(gene_id, sgID_AB, l2fc_su) %>%
pivot_wider(id_cols="sgID_AB", names_from="gene_id", values_from="l2fc_su") %>%
column_to_rownames("sgID_AB") %>%
as.matrix
l2fc_lu_target_gene <- df_l2fc %>%
dplyr::select(gene_id, sgID_AB, l2fc_lu) %>%
pivot_wider(id_cols="sgID_AB", names_from="gene_id", values_from="l2fc_lu") %>%
column_to_rownames("sgID_AB") %>%
as.matrixClusters Indices
idx_genes <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_genes_null <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% NULL_CLUSTERS) %>%
mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_targets <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, target_gene) %$%
sgID_AB
df_col_annots <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
dplyr::select(gene_id, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
dplyr::rename(gene_cluster=cluster_id) %>%
column_to_rownames("gene_id")
df_row_annots <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
dplyr::select(sgID_AB, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
dplyr::rename(target_cluster=cluster_id) %>%
column_to_rownames("sgID_AB")
gaps_col <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_id) %$%
table(cluster_id) %>%
cumsum
gaps_row <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, sgID_AB) %$%
table(cluster_id) %>%
cumsumHeatmaps
SU
pheatmap(l2fc_su_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
LU
pheatmap(l2fc_lu_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
Export Plot
pheatmap(l2fc_su_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_SU_FINAL, width=16, height=16)
pheatmap(l2fc_lu_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_LU_FINAL, width=16, height=16)Example Targets
NUDT21
df_l2fc %>%
filter(str_detect(sgID_AB, "NUDT21")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="NUDT21") +
theme_bw()
CPSF6
df_l2fc %>%
filter(str_detect(sgID_AB, "CPSF6")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="CPSF6") +
theme_bw()
NUDT21 vs CPSF6
SU
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
dplyr::select(gene_id, target_gene, l2fc_su, l2fc_lu, mean_wui) %>%
pivot_wider(id_cols=c("gene_id", "mean_wui"), names_from="target_gene",
values_from=c("l2fc_su", "l2fc_lu")) %>%
ggplot(aes(x=l2fc_su_NUDT21, y=l2fc_su_CPSF6)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(color=mean_wui), size=1) +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="NUDT21 [log2(fold-change SU)]",
y="CPSF6 [log2(fold-change SU)]",
color="Mean WUI") +
theme_bw()
LU
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
dplyr::select(gene_id, target_gene, l2fc_su, l2fc_lu, mean_wui) %>%
pivot_wider(id_cols=c("gene_id", "mean_wui"), names_from="target_gene",
values_from=c("l2fc_su", "l2fc_lu")) %>%
ggplot(aes(x=l2fc_lu_NUDT21, y=l2fc_lu_CPSF6)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(color=mean_wui), size=1) +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="NUDT21 [log2(fold-change LU)]",
y="CPSF6 [log2(fold-change LU)]",
color="Mean WUI") +
theme_bw()
What is that gene in the top right?
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
filter(l2fc_lu > 2) %>%
mutate(gene_name=ens2gene[as.character(gene_id)])## # A tibble: 5 × 11
## gene_id sgID_AB tpm_su l2fc_su tpm_lu l2fc_lu mean_wui target_gene
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000116580.20 CPSF6_+… 7.68 0.286 3.84 2.10 0.127 CPSF6
## 2 ENSG00000163131.12 NUDT21_… 5.10 1.85 7.65 2.48 0.509 NUDT21
## 3 ENSG00000173598.14 CPSF6_+… 33.7 0.280 171. 2.92 0.357 CPSF6
## 4 ENSG00000173598.14 NUDT21_… 42.1 0.600 135. 2.57 0.357 NUDT21
## 5 ENSG00000182093.16 NUDT21_… 3.83 1.45 2.55 2.42 0.236 NUDT21
## # … with 3 more variables: target_gene_id <chr>, cluster_id <chr>,
## # gene_name <fct>Nudt4. This turns out to really be more of the SU isoform, so not as strange as this plot might indicate. The actual LU isoforms fell below the 10% usage minimum.
PAF1 Complex
PAF1
df_l2fc %>%
filter(str_detect(sgID_AB, "PAF1")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="PAF1") +
theme_bw()
RTF1
df_l2fc %>%
filter(str_detect(sgID_AB, "RTF1")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="RTF1") +
theme_bw()
CTR9
df_l2fc %>%
filter(str_detect(sgID_AB, "CTR9")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="CTR9") +
theme_bw()
## Violin Plots ### PAF1 Complex
df_l2fc %>%
filter(str_detect(sgID_AB, "(CTR9|RTF1|PAF1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
CF I
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6|OGFOD1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning: Removed 5 rows containing non-finite values (stat_ydensity).
CPSF Complex
df_l2fc %>%
filter(str_detect(sgID_AB, "(CPSF1|CPSF2|CPSF3|CPSF4|WDR33|FIP1L1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
CF II
df_l2fc %>%
filter(str_detect(sgID_AB, "(PCF11|CLP1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
Nuclear Export Factors
df_l2fc %>%
filter(str_detect(sgID_AB, "(NXF1|THOC)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
Splicing Factors
df_l2fc %>%
filter(str_detect(sgID_AB, "SRSF")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
CSTF
df_l2fc %>%
filter(str_detect(sgID_AB, "CSTF")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
Exosome
df_l2fc %>%
filter(str_detect(sgID_AB, "EXOSC")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning: Removed 1 rows containing non-finite values (stat_ydensity).## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
Proteosome
df_l2fc %>%
filter(str_detect(sgID_AB, "PSM[ABCD]")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning: Removed 1 rows containing non-finite values (stat_ydensity).## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
LSM
df_l2fc %>%
filter(str_detect(sgID_AB, "LSM[0-9]")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
RPL
df_l2fc %>%
filter(str_detect(sgID_AB, "^RPL")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
RPS
df_l2fc %>%
filter(str_detect(sgID_AB, "^RPS")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [3] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [5] IRanges_2.28.0 S4Vectors_0.32.0
## [7] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [9] matrixStats_0.61.0 pheatmap_1.0.12
## [11] forcats_0.5.1 stringr_1.4.0
## [13] dplyr_1.0.8 purrr_0.3.4
## [15] readr_2.1.1 tidyr_1.1.4
## [17] tibble_3.1.7 ggplot2_3.3.5
## [19] tidyverse_1.3.1 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
## [4] bit64_4.0.5 RColorBrewer_1.1-2 httr_1.4.2
## [7] tools_4.1.1 backports_1.4.0 bslib_0.3.1
## [10] utf8_1.2.2 R6_2.5.1 DBI_1.1.1
## [13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
## [16] bit_4.0.4 compiler_4.1.1 cli_3.3.0
## [19] rvest_1.0.2 xml2_1.3.3 DelayedArray_0.20.0
## [22] labeling_0.4.2 sass_0.4.0 scales_1.1.1
## [25] digest_0.6.29 rmarkdown_2.11 XVector_0.34.0
## [28] pkgconfig_2.0.3 htmltools_0.5.2 dbplyr_2.1.1
## [31] fastmap_1.1.0 highr_0.9 rlang_1.0.2
## [34] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
## [37] generics_0.1.1 farver_2.1.0 jsonlite_1.7.2
## [40] vroom_1.5.7 RCurl_1.98-1.5 GenomeInfoDbData_1.2.7
## [43] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
## [46] fansi_0.5.0 lifecycle_1.0.1 stringi_1.7.6
## [49] yaml_2.2.1 zlibbioc_1.40.0 grid_4.1.1
## [52] parallel_4.1.1 crayon_1.4.2 lattice_0.20-45
## [55] haven_2.4.3 hms_1.1.1 knitr_1.39
## [58] pillar_1.7.0 reprex_2.0.1 glue_1.6.2
## [61] evaluate_0.15 modelr_0.1.8 vctrs_0.4.1
## [64] tzdb_0.2.0 cellranger_1.1.0 gtable_0.3.0
## [67] assertthat_0.2.1 xfun_0.30 broom_0.8.0
## [70] viridisLite_0.4.0 ellipsis_0.3.2Conda Environment
## Conda Environment YAML
name: base
channels:
- conda-forge
- bioconda
- defaults
dependencies:
- anaconda-client=1.8.0=pyhd8ed1ab_0
- anaconda-project=0.10.2=pyhd8ed1ab_0
- attrs=21.2.0=pyhd8ed1ab_0
- awscli=1.25.79=py39h6e9494a_0
- backports=1.0=py_2
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
- backports.zoneinfo=0.2.1=py39h701faf5_5
- bagit=1.8.1=pyhd8ed1ab_0
- bagit-profile=1.3.1=pyhd8ed1ab_0
- bdbag=1.6.1=pyhd8ed1ab_0
- beautifulsoup4=4.9.3=pyhb0f4dca_0
- blinker=1.4=py_1
- boa=0.13.0=pyha770c72_0
- boolean.py=3.7=py_0
- boto3=1.24.78=pyhd8ed1ab_0
- botocore=1.27.78=pyhd8ed1ab_0
- brotlipy=0.7.0=py39h63b48b0_1004
- bzip2=1.0.8=h0d85af4_4
- c-ares=1.18.1=h0d85af4_0
- ca-certificates=2022.9.24=h033912b_0
- cachecontrol=0.12.11=pyhd8ed1ab_0
- cairo=1.16.0=he43a7df_1008
- cctools=973.0.1=hd9211c8_2
- cctools_osx-64=973.0.1=h3e07e27_2
- certifi=2022.9.24=pyhd8ed1ab_0
- cffi=1.15.1=py39hae9ecf2_0
- chardet=5.0.0=py39h6e9494a_0
- charset-normalizer=2.0.0=pyhd8ed1ab_0
- click=8.1.3=py39h6e9494a_0
- clyent=1.2.2=py_1
- colorama=0.4.3=py_0
- commonmark=0.9.1=py_0
- conda=4.14.0=py39h6e9494a_0
- conda-build=3.21.9=py39h6e9494a_1
- conda-forge-pinning=2021.10.10.22.03.30=hd8ed1ab_0
- conda-libmamba-solver=22.6.0=pyhd8ed1ab_0
- conda-pack=0.6.0=pyhd3deb0d_0
- conda-package-handling=1.9.0=py39ha30fb19_0
- conda-smithy=3.17.2=pyhd8ed1ab_0
- conda-standalone=4.10.3=h694c41f_0
- conda-suggest=0.1.1=pyh9f0ad1d_0
- conda-suggest-conda-forge=2021.8.24=h694c41f_0
- conda-verify=3.1.1=py39h6e9494a_1004
- constructor=3.3.1=py39h6e9494a_0
- cryptography=38.0.3=py39h7eb6a14_0
- curl=7.86.0=h57eb407_1
- dataclasses=0.8=pyhc8e2a94_3
- dbus=1.13.6=ha13b53f_2
- deprecated=1.2.12=pyh44b312d_0
- docutils=0.16=py39h6e9494a_3
- expat=2.4.1=he49afe7_0
- ffq=0.2.1=pyhdfd78af_0
- filelock=3.0.12=pyh9f0ad1d_0
- fmt=9.1.0=hb8565cd_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
- font-ttf-source-code-pro=2.038=h77eed37_0
- font-ttf-ubuntu=0.83=hab24e00_0
- fontconfig=2.13.1=h10f422b_1005
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
- freetype=2.10.4=h4cff582_1
- fribidi=1.0.10=hbcb3906_0
- frozendict=2.3.4=py39h701faf5_0
- future=0.18.2=py39h6e9494a_5
- gawk=5.1.0=h8a989fb_0
- gdk-pixbuf=2.42.6=h2e6141f_0
- gettext=0.21.1=h8a4c099_0
- giflib=5.2.1=hbcb3906_2
- git=2.34.1=pl5321h9a53687_0
- git-lfs=2.13.3=h694c41f_0
- gitdb=4.0.7=pyhd8ed1ab_0
- gitpython=3.1.18=pyhd8ed1ab_0
- glib=2.70.2=hcf210ce_0
- glib-tools=2.70.2=hcf210ce_0
- glob2=0.7=py_0
- globus-sdk=2.0.1=pyhd8ed1ab_0
- gmp=6.2.1=h2e338ed_0
- gnutls=3.6.13=h756fd2b_1
- graphite2=1.3.13=h2e338ed_1001
- harfbuzz=2.9.0=h159f659_0
- htop=3.2.1=h398481e_0
- htslib=1.15=hc057d7f_0
- hub=2.14.2=hc7d050b_0
- icu=68.1=h74dc148_0
- idna=3.1=pyhd3deb0d_0
- importlib-metadata=4.11.4=py39h6e9494a_0
- importlib_metadata=4.11.4=hd8ed1ab_0
- importlib_resources=5.4.0=pyhd8ed1ab_0
- inotify_simple=1.3.5=pyha770c72_3
- ipython_genutils=0.2.0=py_1
- isodate=0.6.0=py_1
- jbig=2.1=h0d85af4_2003
- jinja2=3.0.1=pyhd8ed1ab_0
- jmespath=0.10.0=pyh9f0ad1d_0
- joblib=1.0.1=pyhd8ed1ab_0
- jpeg=9d=hbcb3906_0
- json5=0.9.5=pyh9f0ad1d_0
- jsonschema=4.3.1=pyhd8ed1ab_0
- jupyter_core=4.11.1=py39h6e9494a_0
- krb5=1.19.3=hb49756b_0
- ld64=609=hd2e7500_2
- ld64_osx-64=609=h2487922_2
- ldid=2.1.2=h6a69015_3
- lerc=2.2.1=h046ec9c_0
- libarchive=3.5.2=h2b60450_1
- libcurl=7.86.0=h57eb407_1
- libcxx=14.0.6=hccf4f1f_0
- libdeflate=1.10=h0d85af4_0
- libedit=3.1.20191231=h0678c8f_2
- libev=4.33=haf1e3a3_1
- libffi=3.4.2=h0d85af4_5
- libglib=2.70.2=hf1fb8c0_0
- libiconv=1.17=hac89ed1_0
- libidn2=2.3.2=h0d85af4_0
- liblief=0.11.5=he49afe7_0
- libllvm12=12.0.1=hd011deb_2
- libmamba=1.0.0=h2bf831e_2
- libmambapy=1.0.0=py39he069e75_2
- libnghttp2=1.47.0=h7cbc4dc_1
- libpng=1.6.37=h7cec526_2
- librsvg=2.50.7=hd2a7919_0
- libsolv=0.7.22=hd9580d2_0
- libssh2=1.10.0=h7535e13_3
- libtiff=4.3.0=h1167814_0
- libunistring=0.9.10=h0d85af4_0
- libwebp-base=1.2.1=h0d85af4_0
- libxml2=2.9.12=h93ec3fd_0
- libxslt=1.1.33=h5739fc3_2
- libzlib=1.2.13=hfd90126_4
- license-expression=1.2=py_0
- lockfile=0.12.2=py_1
- lxml=4.8.0=py39h63b48b0_2
- lz4-c=1.9.3=he49afe7_1
- lzo=2.10=haf1e3a3_1000
- mamba=1.0.0=py39ha435c47_2
- markupsafe=2.1.1=py39h63b48b0_1
- msgpack-python=1.0.4=py39h92daf61_1
- msrest=0.6.21=pyh44b312d_0
- nbformat=5.1.3=pyhd8ed1ab_0
- ncurses=6.3=h96cf925_1
- nettle=3.6=hedd7734_0
- oauthlib=3.1.1=pyhd8ed1ab_0
- openssl=1.1.1s=hfd90126_0
- pango=1.48.9=ha05cd14_0
- patch=2.7.6=hbcf498f_1002
- pcre=8.45=he49afe7_0
- pcre2=10.37=ha16e1b2_0
- perl=5.32.1=0_h0d85af4_perl5
- pigz=2.6=h5dbffcc_0
- pip=21.2.4=pyhd8ed1ab_0
- pixman=0.40.0=hbcb3906_0
- pkginfo=1.7.1=pyhd8ed1ab_0
- popt=1.16=h7b079dc_2002
- prompt-toolkit=3.0.20=pyha770c72_0
- prompt_toolkit=3.0.20=hd8ed1ab_0
- psutil=5.9.2=py39ha30fb19_0
- py-lief=0.11.5=py39h9fcab8e_0
- pyasn1=0.4.8=py_0
- pybind11-abi=4=hd8ed1ab_3
- pycosat=0.6.3=py39h63b48b0_1010
- pycparser=2.20=pyh9f0ad1d_2
- pycrypto=2.6.1=py39h89e85a6_1006
- pygithub=1.53=py_0
- pygments=2.10.0=pyhd8ed1ab_0
- pyjwt=1.7.1=py_0
- pyrsistent=0.18.1=py39h63b48b0_1
- pysocks=1.7.1=pyha2e5f31_6
- python=3.9.13=h57e37ff_0_cpython
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python-libarchive-c=4.0=py39h6e9494a_1
- python-tzdata=2021.5=pyhd8ed1ab_0
- python_abi=3.9=2_cp39
- pytz=2021.1=pyhd8ed1ab_0
- pytz-deprecation-shim=0.1.0.post0=py39h6e9494a_2
- pyyaml=5.4.1=py39h701faf5_3
- readline=8.1.2=h3899abd_0
- reproc=14.2.3=h0d85af4_0
- reproc-cpp=14.2.3=he49afe7_0
- requests=2.28.1=pyhd8ed1ab_1
- requests-oauthlib=1.3.0=pyh9f0ad1d_0
- rich=10.16.1=pyhd8ed1ab_0
- ripgrep=13.0.0=h244e342_0
- rsa=4.7.2=pyh44b312d_0
- rsync=3.2.7=ha1fed10_0
- ruamel.yaml=0.17.21=py39h63b48b0_1
- ruamel.yaml.clib=0.2.6=py39h63b48b0_1
- ruamel_yaml=0.15.80=py39h701faf5_1007
- s3transfer=0.6.0=pyhd8ed1ab_0
- scrypt=0.8.18=py39hbfd427f_4
- setuptools=65.3.0=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
- smartmontools=7.2=h940c156_0
- smmap=3.0.5=pyh44b312d_0
- soupsieve=2.3.1=pyhd8ed1ab_0
- sqlite=3.38.5=hd9f0692_0
- tapi=1100.0.11=h9ce4665_0
- tk=8.6.12=h5dbffcc_0
- toolz=0.11.1=py_0
- tornado=6.2=py39h701faf5_0
- tqdm=4.62.2=pyhd8ed1ab_0
- traitlets=5.1.0=pyhd8ed1ab_0
- typing_extensions=3.10.0.0=pyha770c72_0
- tzdata=2021e=he74cb21_0
- tzlocal=4.2=py39h6e9494a_1
- urllib3=1.26.6=pyhd8ed1ab_0
- vsts-python-api=0.1.22=py_0
- watchgod=0.7=pyhd8ed1ab_0
- wcwidth=0.2.5=pyh9f0ad1d_2
- wget=1.20.3=h52ee1ee_1
- wheel=0.37.0=pyhd8ed1ab_1
- wrapt=1.14.1=py39h701faf5_0
- xxhash=0.8.0=h35c211d_3
- xz=5.2.5=haf1e3a3_1
- yaml=0.2.5=haf1e3a3_0
- yaml-cpp=0.7.0=hb486fe8_1
- zipp=3.5.0=pyhd8ed1ab_0
- zlib=1.2.13=hfd90126_4
- zstd=1.5.2=hfa58983_4
- pip:
- pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3